IEEE Systems, Man, and Cybernetics Magazine - January 2018 - 18

For data sets to
be considered big
data, there should
be a complete set
of rules, structures,
and relationships
about a particular
object in a problem.

Therefore, data collaboration may create big data. The
story of the blind men and an elephant demonstrates the
importance of data collaboration, i.e., if all the data
collected by the blind men are integrated through a collaborative effort, then the concept of the elephant is correctly established.
Collaboration develops and contributes data to the
computer world. All of the data collected in digital media
throughout the world were contributed by the combined
efforts of people and technologies over the period since
computer storage became widely available. From this perspective, big data is large enough to include all of the
required information to provide an abstract model. That is
why R. Fouler, the chief executive officer of VoloMetrix,
stated, "Applying big data and cutting edge analytics technologies introspectively to internal collaboration data represents the next frontier" [1]. He emphasizes the importance
of collaboration data from many enterprises. In fact, such
collaboration data, if large enough, will contribute to the
establishment of abstract collaboration methodologies,
rules, policies, and models for enterprises and make the
collaboration data more valuable.
From the aforementioned discussion, big data presents
both challenges and resources. If we consider big data
challenges, we need innovative methodologies or technologies to process data, i.e., data collaboration. If we take big
data as resources, we need to mine from data those elements that are valuable for collaboration. In both aspects,
RBC and E-CARGO are promising contributions.
RBC is an emerging computational methodology that
uses roles as a primary underlying mechanism to facilitate
collaborative activities [8]. RBC and the E-CARGO model
bring in new visions to a collaborative system by dividing
the system into different, well-specified components. Relationships among these components can be formalized
through mathematical symbolization. In this way, problems previously considered to be too complicated for solutions by computer-based systems can now be well defined
and, finally, solved.
18

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Janu ar y 20 18

Different data sets may play various roles in data collaboration, the goal of which is to form big data. Based on
this requirement, the E-CARGO model is an effective
model to support data collaboration. With the E-CARGO
model, a system S can be defined as a nine-tuple
R : : = < C, O, A, M, R, E, G, S 0, H >, where C is a set of
classes, O is a set of objects, A is a set of agents, M is a
set of messages, R is a set of roles, E is a set of environments, G is a set of groups, S0 is the initial state of the
system, and H is a set of users. In such a system, A and
H and E and G are tightly coupled sets. Human users
and their agents may perform roles together. Every group
should work in an environment that regulates the group.
E-CARGO is an abstract model and is developed continuously, and investigations may emphasize different aspects
in different ways.
With the E-CARGO model, big data can be viewed as a
system that creates valuable information ^C h from data
items ^Oh by collaboration among a group ^Gh of data
sets ^A h playing different roles ^Rh in an environment
^ E h. A data set is composed of data items. One data set
cannot be qualified to create useful information (classes).
The environment is the requirement for data engineers to
specify before data collaboration. For example, in a criminal case investigation, we need a group ^Gh of different
data sets ^A h, including the information recorded by cameras (a 1), the data in the criminal databases (a 2), and the
residence data from the databases of surrounding areas
(a 3). To start the investigation, we need to establish an
environment ^Eh to include roles ^Rh such as current situations (r1), historical properties (r2), and surroundings
(r3) . By matching agents with roles, we may extract the
data items ^Oh that qualify the roles, and then, by the
intersections of these objects, we may extract C [the criminal properties extracted from all the data sets ^Oh] to be
the goal of this data collaboration. From the life cycle of
RBC, i.e., role negotiation, agent evaluation, role assignment, role execution, and role transfer, we may find the connections with data collaboration.



Table of Contents for the Digital Edition of IEEE Systems, Man, and Cybernetics Magazine - January 2018

Contents
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